International Journal of Approximate Reasoning最新文献

英文中文

Matrix-based local multigranulation reduction for covering decision information systems

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-03-14 DOI: 10.1016/j.ijar.2025.109415

Tao Jiang , Yan-Lan Zhang

Attribute reduction has become an essential step in pattern recognition and machine learning tasks. As an extension of the classical rough set, the covering rough set has garnered considerable attention in both theory and application. A matrix-based method for computing local covering optimistic approximation sets and local optimistic multigranulation reductions based on covering rough set in covering decision information systems (CDISs) is proposed in this paper. Firstly, we introduce a matrix representation along with its associated operations to compute the local covering optimistic approximation sets and the local positive regions of the CDISs. Subsequently, local optimistic discernibility matrices and local optimistic discernibility functions are constructed for the CDISs. By performing disjunction and conjunction operations on these local optimistic discernibility matrices, all local optimistic multigranulation reductions of the CDISs can be accurately obtained. In addition, an algorithm is developed using the local optimistic discernibility matrix to compute a suboptimal minimal local optimistic multigranulation reduction. Finally, to verify the effectiveness and feasibility of the proposed method, numerical experiments are conducted on 6 UCI datasets.

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引用次数: 0

A novel axiomatic approach to L-valued rough sets within an L-universe via inner product and outer product of L-subsets

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-03-14 DOI: 10.1016/j.ijar.2025.109416

Lingqiang Li, Qiu Jin

The fuzzy rough approximation operator serves as the cornerstone of fuzzy rough set theory and its practical applications. Axiomatization is a crucial approach in the exploration of fuzzy rough sets, aiming to offer a clear and direct characterization of fuzzy rough approximation operators. Among the fundamental tools employed in this process, the inner product and outer product of fuzzy sets stand out as essential components in the axiomatization of fuzzy rough sets. In this paper, we will develop the axiomatization of a comprehensive fuzzy rough set theory, that is, the so-called L-valued rough sets with an L-set serving as the foundational universe (referred to as the L-universe) for defining L-valued rough approximation operators, where L typically denotes a GL-quantale. Firstly, we give the notions of inner product and outer product of two L-subsets within an L-universe and examine their basic properties. It is shown that these notions are extensions of the corresponding notion of fuzzy sets within a classical universe. Secondly, leveraging the inner product and outer product of L-subsets, we respectively characterize L-valued upper and lower rough approximation operators generated by general, reflexive, transitive, symmetric, Euclidean, and median L-value relations on L-universe as well as their compositions. Finally, utilizing the provided axiomatic characterizations, we present the precise examples for the least and largest equivalent L-valued upper and lower rough approximation operators. Notably, many existing axiom characterizations of fuzzy rough sets within classical universe can be viewed as direct consequences of our findings.

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引用次数: 0

Semi-supervised hierarchical multi-label classifier based on local information

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-03-10 DOI: 10.1016/j.ijar.2025.109411

Jonathan Serrano-Pérez , L. Enrique Sucar

Scarcity of labeled data is a common problem in supervised classification, since hand-labeling can be time consuming, expensive or hard to label; on the other hand, large amounts of unlabeled information can be found. The problem of scarcity of labeled data is even more notorious in hierarchical classification, because the data of a node is split among its children, which results in few instances associated to the deepest nodes of the hierarchy. In this work it is proposed the semi-supervised hierarchical multi-label classifier based on local information (SSHMC-BLI) which can be trained with labeled and unlabeled data to perform hierarchical classification tasks. The method can be applied to any type of hierarchical problem, here we focus on the most difficult case: hierarchies of DAG type, where the instances can be associated to multiple paths of labels which can finish in an internal node. SSHMC-BLI builds pseudo-labels for each unlabeled instance from the paths of labels of its labeled neighbors, while it considers whether the unlabeled instance is similar to its neighbors. Experiments on 12 challenging datasets from functional genomics show that making use of unlabeled along with labeled data can help to improve the performance of a supervised hierarchical classifier trained only on labeled data, even with statistical significance.

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引用次数: 0

Characterizations for union and intersection on non-normal membership functions of type-2 fuzzy sets

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-03-10 DOI: 10.1016/j.ijar.2025.109414

Zhi-qiang Liu , Jingxin Liu

In this work, we mainly investigate set operations for type-2 fuzzy sets. To be more exact, we present several algorithms under the left continuous t-norms that compute the join and meet operations of the non-normal convex secondary membership functions of type-2 fuzzy sets, and give some properties of operations that would enhance the application of fuzzy logic connectives. We anticipate that these algorithms can be applied to type-2 fuzzy logic systems as well as several fields of soft computing that tackle logical operations in type-2 fuzzy sets.

在这项工作中，我们主要研究 2 型模糊集的集合运算。更确切地说，我们提出了几种左连续 t 准则下的算法，用于计算 2 型模糊集的非正态凸次成员函数的连接和相遇运算，并给出了一些运算属性，这些属性将增强模糊逻辑连接词的应用。我们预计，这些算法可应用于 2 型模糊逻辑系统以及处理 2 型模糊集逻辑运算的多个软计算领域。

引用次数: 0

Hyperspace approach to relation-based neighborhood operators

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-03-07 DOI: 10.1016/j.ijar.2025.109404

Marian Przemski

Recently, new, subsequent versions of neighborhoods have been defined based on the concept of relation-based neighborhoods introduced by Y.Y. Yao. This article proposes a unified concept for investigations of such neighborhoods. This work presents the notion of hyper-neighborhood, which enables the investigation of the neighborhoods from the universe's perspective. As a result, we drive multiple equivalent characterizations of the types of neighborhoods that enable us to compare them and indicate the new, missing kinds of neighborhoods. Moreover, many kinds of neighborhoods defined in the literature on the issue proved to be identical. In particular, none of the types of recently defined so-called subset neighborhoods is new.

引用次数: 0

Evidential time-to-event prediction with calibrated uncertainty quantification

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-03-04 DOI: 10.1016/j.ijar.2025.109403

Ling Huang , Yucheng Xing , Swapnil Mishra , Thierry Denœux , Mengling Feng

Time-to-event analysis provides insights into clinical prognosis and treatment recommendations. However, this task is more challenging than standard regression problems due to the presence of censored observations. Additionally, the lack of confidence assessment, model robustness, and prediction calibration raises concerns about the reliability of predictions. To address these challenges, we propose an evidential regression model specifically designed for time-to-event prediction. Our approach computes a degree of belief for the event time occurring within a time interval, without any strict distribution assumption. Meanwhile, the proposed model quantifies both epistemic and aleatory uncertainties using Gaussian Random Fuzzy Numbers and belief functions, providing clinicians with uncertainty-aware survival time predictions. Experimental evaluations using simulated and real-world survival datasets highlight the potential of our approach for enhancing clinical decision-making in survival analysis.

引用次数: 0

Multi-view outlier detection based on multi-granularity fusion of fuzzy rough granules

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-03-02 DOI: 10.1016/j.ijar.2025.109402

Siyi Qiu , Yuefei Wang , Zixu Wang , Jinyan Cao , Xi Yu

In recent years, multi-view data has seen widespread application across various fields, presenting both opportunities and challenges due to its complex distribution across different views. Detecting outliers in such heterogeneous data has become a significant research problem. Existing multi-view outlier detection methods often rely on clustering assumptions, pairwise constraints between views, and a focus on learning consensus information, which overlook the inherent differences across views. To address the aforementioned issues, this paper proposes an outlier detection method based on the fusion of multi-granularity fuzzy rough information (MGFMOD). The method calculates a multi-granularity similarity matrix using fuzzy similarity relationships, combines similarity matrices from different granularities to form an upper approximation matrix, and constructs fused upper approximation granules to detect attribute anomalies. Neighbor domain probabilistic mapping is then employed to unify neighborhood relationships across views, allowing the analysis of both consistency and distribution differences to capture class outliers. Additionally, this paper employs a novel coarse-to-fine approximation method to construct the upper approximation matrix, further improving the accuracy of attribute outlier detection. Experimental results on multiple public datasets demonstrate that the proposed method generally outperforms existing multi-view outlier detection methods in terms of detection accuracy and robustness.

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引用次数: 0

Multiindistinguishability operators 多区分性算子

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-02-28 DOI: 10.1016/j.ijar.2025.109401

D. Boixader, J. Recasens

In this paper (binary) equivalence relations and their fuzzification, indistinguishability operators, are generalized to n-equivalence relations and n-multiindistinguishability operators respectively. Some of the properties of these two last objects are stated as well as their relation with binary ones.

引用次数: 0

DEEM: A novel approach to semi-supervised and unsupervised image clustering under uncertainty using belief functions and convolutional neural networks

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-02-27 DOI: 10.1016/j.ijar.2025.109400

Loïc Guiziou , Emmanuel Ramasso , Sébastien Thibaud , Sébastien Denneulin

DEEM (Deep Evidential Encoding of iMages) is a clustering algorithm that combines belief functions with convolutional neural networks in a Siamese-like framework for unsupervised and semi-supervised image clustering. In DEEM, images are mapped to Dempster–Shafer mass functions to quantify uncertainty in cluster membership. Various forms of prior information, including must-link and cannot-link constraints, supervised dissimilarities, and Distance Metric Learning, are incorporated to guide training and improve generalisation. By processing image pairs through shared network weights, DEEM aligns pairwise dissimilarities with the conflict between mass functions, thereby mitigating errors in noisy or incomplete distance matrices. Experiments on MNIST demonstrate that DEEM generalises effectively to unseen data while managing different types of prior knowledge, making it a promising approach for clustering and semi-supervised learning from image data under uncertainty.

引用次数: 0

Soft computing for the posterior of a matrix t graphical network

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-02-24 DOI: 10.1016/j.ijar.2025.109397

Jason Pillay , Andriette Bekker , Johannes Ferreira , Mohammad Arashi

Modeling noisy data in a network context remains an unavoidable obstacle; fortunately, random matrix theory may comprehensively describe network environments. Noisy data necessitates the probabilistic characterization of these networks using matrix variate models. Denoising network data using a Bayesian approach is not common in surveyed literature. Therefore, this paper adopts the Bayesian viewpoint and introduces a new version of the matrix variate t graphical network. This model's prior beliefs rely on the matrix variate gamma distribution to handle the noise process flexibly; from a statistical learning viewpoint, such a theoretical consideration benefits the comprehension of structures and processes that cause network-based noise in data as part of machine learning and offers real-world interpretation. A proposed Gibbs algorithm is provided for computing and approximating the resulting posterior probability distribution of interest to assess the considered model's network centrality measures. Experiments with synthetic and real-world stock price data are performed to validate the proposed algorithm's capabilities and show that this model has wider flexibility than the model proposed by [13].

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引用次数: 0

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